Development of predictive coding networks for spatial navigation Summary: Mammalian navigation uses internal models to predict the spatial-temporal statistical regularity of the sequence of environmental locations. Predictive coding theories view the brain as Bayesian interpreter that computes the difference between the external stimuli and an internal model of the world. It is increasingly understood that sequential spatial information is represented by temporal sequences of ensemble neuronal firing in the hippocampus. Without exception, these ensemble patterns have been investigated exclusively in adult animals. A small handful of studies measured the activity of individual hippocampal neurons as pre- weanling or juvenile animals briefly explored open-field environments. However, spatial experience is believed to be internally represented by highly compressed temporal sequences of neuronal ensemble firing during awake and sleep states in the form of theta sequences, replay, and preplay, which are expressed within populations rather than single neurons. Our goal is to reveal the principles and stages of early-life development and maturation of attractor-based compressed temporal sequences as priors for encoding future navigation experiences (i.e., predictive coding), and the role age and early spatial experience play in these processes and in navigation learning. To achieve this goal, we develop new methods to: 1. Chronically record, at millisecond resolution, from large ensembles of neurons (up to 70 simultaneously) from the hippocampus in developing freely-behaving and sleeping rats from first day after eye opening; 2. Reveal and analyze predictive coding network properties; 3. Control and restrict animals? prior spatial experience. Successful completion of this proposal will provide unique resources to help understand the emergence and maturation of cortical networks for internally-generated representations and will provide links and predictive models to study perturbations in neuronal ensemble patterns underlying neurodevelopmental and psychiatric disorders like schizophrenia and autism spectrum disorder.